The present invention relates to noise estimation domain in particular in the domain of image or video processing.
The approaches described in this section could be pursued, but are not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section. Furthermore, all embodiments are not necessarily intended to solve all or even any of the problems brought forward in this section.
A possible way to estimate the amount of noise is to use robust statistical method. For instance, (see Donoho, David L.; lain M. Johnstone, December 1995, “Adapting to Unknown Smoothness via Wavelet Shrinkage”. Journal of the American Statistical Association Vol. 90, No. 432: 1200-1244), a possible way to estimate a noise level of an image when the noise is white noise consists in computing the median of a high-pass subband.
The underlying expectation is that the median is carrying only information about the noise, and that the signal is sparse enough to have little impact on this median. Therefore, coefficients (in which the signal is relevant) represent less than few percent of the coefficient population).
In particular, the median of the absolute values of coefficients may be proportional to the noise standard deviation. This median may be scaled with an appropriate constant to yield an estimate of the standard deviation of the Gaussian noise.σ=median(|s|)×f with s the signal
The factor f (for instance f=1/0.67) may be estimated by measuring the median of values drawn from a known Gaussian generator of known standard deviation.
Such estimation has drawbacks as this expectation (i.e that the signal is sparsely represented in the high pass subband) is not always adequate.
Indeed, when the image is highly textured, or contains parts that are highly textured, the median may produce an estimate of the noise level that is much higher than what it ought to be: when a median is computed over a highly textured image, state of the art methods overestimate the amount of noise, apply a stronger than necessary noise reduction and wash out the textures that were confused with noise by the noise estimation algorithm.
There is thus a need for having a noise estimation method robust even if parts of the images are highly textured and then to improve image quality through a more robust noise level estimation leading to smother noise reduction, and less aggressive flattening of textures in the highly textured images.